Developing an ontology-based classification for mobility among individuals with acquired brain injury
Bibliographic record
Abstract
Acquired brain injury (ABI), including traumatic brain injury (TBI) and stroke, is a leading cause of disability in Canada. Over 60% of the 1.5 million Canadians with ABI that go through the care continuum annually report ongoing restrictions in mobility and participation in societal roles. Planning rehabilitation intervention requires an understanding of the nature and severity of mobility challenges among individuals with ABI through a comprehensive evaluation of mobility. Thus, this PhD work comprises four studies, all addressing the global objective “to provide a common language and taxonomy of mobility to help compare and select mobility measures for clinical care and research among individuals with ABI (Stroke and TBI)”. The objective of Manuscript 1 was to synthesize the measurement properties, the interpretability, and the feasibility of mobility measures, from various sources of information (patients, clinicians, technology), through an umbrella review of published systematic reviews among individuals with ABI. Given that the umbrella review may not cover all measures that evaluate the determinants influencing mobility, focus group discussions were conducted among clinicians, individuals with ABI, and their caregivers. Thus, the objective of Manuscript 2 was to identify factors influencing mobility which need to be considered while evaluating mobility, and incorporating patients' needs and preferences into individualized care management plans, as perceived by clinicians, individuals with ABI, and their caregivers. Results of the focus groups identified the measures used in clinical practice and the determinants that influence mobility among individuals with ABI. Given that the care process emerged when we explored factors influencing mobility evaluation with clinicians, individuals with ABI, and their caregivers, Manuscript 3 aimed to explore the care experiences and service design related to rehabilitation for mobility and participation in the community among individuals with ABI, as perceived by clinicians, individuals with ABI, and their caregivers. Perspectives from clinicians, individuals with ABI, and their caregivers identified mobility factors related to service provisions, which are classified as environmental factors in the ICF that may improve mobility rehabilitation from the acute level of care to community re-integration among individuals with ABI. Manuscripts 1 and 2 synthesized critical information to define the breadth of mobility measures; Manuscripts 2 and 3 identified determinants that influence mobility, reflecting that mobility is a multidimensional construct affected by the interactions between Body Functions, Activity and Participation, and Contextual Factors. This complexity of measuring mobility, given that it is a multidimensional construct, requires robust strategies for organizing and effectively curating scientific knowledge to enable aggregation and comparison of findings across research studies. Natural language processing (NLP) is one approach that can be used to properly classify pre-defined content from mobility measures to understand knowledge evolution and correctly reflect and evolve our understanding of mobility. Thus, the objective of Manuscript 4 was to identify a comprehensive outcome set and develop preliminary banks of items of mobility among individuals with ABI, using NLP.Results of all Manuscripts will generate scientific evidence of useful knowledge related to standardizing terms and labels for mobility (common language) that will inform the creation of a Core Outcome Set and develop the ontology for mobility. The ontology of mobility will help reduce heterogeneity in terms related to mobility, making it easier for researchers, clinicians, and patients to identify a Core Outcome Set of mobility domains important to measure in clinical care and research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".